Inaccuracies of shape averaging method using dynamic time warping for time series data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4487 LNCS, Issue , 2007, Pages 513-520

  Niennattrakul, Vit ^a   Ratanamahatana, Chotirat Ann ^a  

^a CHULALONGKORN UNIVERSITY   (Thailand)

Author keywords

Dynamic time warping; Shape averaging; Time series

Indexed keywords

DISTANCE MEASUREMENT; MAPPING; TIME SERIES ANALYSIS;

DYNAMIC TIME WARPING; SHAPE AVERAGING;

DATA MINING;

EID: 37249046121     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-72584-8_68     Document Type: Conference Paper

References (19)

1. Abdulla, W.H., Chow, D., and Sin, G. Cross-words reference template for DTW-based speech recognition systems. In Proc. of TENCON 2003 (2003)
2. Bagnall, A. and Janacek, G. 2005. Clustering Time Series with Clipped Data. Mach. Learn. 58 (2005) 151-178
3. Boudaoud, S., Rix, H., and Meste, O. Integral shape averaging and structural average estimation. IEEE Trans. on Signal Processing, vol.53, no10 (2005) 3644-3650
4. Bradley, P. S., and Fayyad, U.M. Refining Initial Points for K-Means Clustering. In Proceedings of the 15th Int'l Conference on Machine Learning (1998) 91-99
5. Caiani, E.G., Porta, A., Baselli, G., Turiel, M., Muzzupappa, S., Pieruzzi, F., Crema, C., Malliani, A. and Cerutti, S. Warped-average template technique to track on a cycle-bycycle basis the cardiac filling phases on left ventricular volume. IEEE Computers in Cardiology (1998)
6. Corradini, A. Dynamic Time Warping for Off-Line Recognition of a Small Gesture Vocabulary. In Proc. of the IEEE ICCV Workshop on Ratfg-Rts. Washington, DC (2001)
7. Chu, S., Keogh, E., Hart, and D., Pazzani, M. Iterative deepening dynamic time warping for time series. In Proceedings of SIAM International Conference on Data Mining (2002)
8. Gupta, L., Molfese, D.L., Tammana, R., and Simos, P.G. Nonlinear alignment and averaging for estimating the evoked potential. IEEE Trans. on Biomed. Eng. vol.43, no.4 (1996) 348-356
9. Hu, J. and Ray, B. An Interleaved HMM/DTW Approach to Robust Time Series Clustering. IBM T.J. Watson Research Center (2006)
10. Keogh, E. and Smyth, P. An enhanced representation of time series which allows fast classification, clustering and relevance feedback. KDD (1997) 24-30
11. Lange, D.H., Pratt, H., and Inbar, G.F. (1997). Modeling and estimation of single evoked brain potential components. IEEE Trans. on Biomed. Eng. Vol.44 (1997) 791-799
12. Mor-Avi, V., Gillesberg, I.E., Korcarz, C., Sandelski, J., Lang, R.M. Signal averaging helps reliable noninvasive monitoring of left ventricular dimensions based on acoustic quantification. Computers in Cardiology (1994) 21-24
13. Oates, T., Firoiu, L., and Cohen, P.R. Using Dynamic Time Warping to Bootstrap HMM-Based Clustering of Time Series. In Sequence Learning Paradigms, Algorithms, and Applications. Volume 1828 of Lecture Notes in Computer Science (2001) 35-52
14. Rabiner, L. R., Levinson, S. E., Rosenberg, A. E., and Wilpon, J. G. Speaker-independent recognition of isolated words using clustering techniques. In Readings in Speech Recognition, CA (1990) 166-179
15. Ratanamahatana, C.A. and Keogh, E. Everything you know about Dynamic Time Warping is Wrong. In Proc. of KDD Workshop on Mining Temporal and Sequential Data (2004)
16. th ICADL (2005)
17. Saito, N. Local feature extraction and its application using a library of bases. PhD thesis, Yale University (1994)
18. Salvador, S. and Chan, P. FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space. In Proc. of KDD Workshop on Mining Temporal and Sequential Data (2004)
19. Wilpon, J. and Rabiner, L. A modified K-means clustering algorithm for use in isolated work recognition. IEEE Trans. on Signal Processing, vol.33 (1985) 587-594